A novel predictive approach to prosthetic control for digit amputations using grip classification

Abstract

In this paper, the authors present a novel method of prosthetic control for amputees who are missing a subset of their digits. The core of the system is a personalized set of basic configurations of the human hand to facilitate given tasks, referred to as use-patterns. In the concept, the system first senses the positions of the remaining, healthy digits using resistive flex sensors mounted onto a glove. Then, an algorithm using a Zscore metric compares the flex sensor readings to a large set of training data to determine which of several use-patterns the current situation corresponds to. A control system can then move a bionic digit into the intended position corresponding to the usepattern detected, completing the grip. MATLAB simulations were run to determine the accuracy of this concept on five exemplary use-patterns. The success rate with one amputated digit was 96.5% and the success rate with two amputated digits was 92.5%. This demonstrates the feasibility of a simple, intuitive, and lowcost prosthetic control system based on this premise that could deliver even higher success rates when personalized with selflearning algorithms.

Resistive flex sensors: a survey

“…The last work concerning prosthetic control including the use of RFSs was developed for amputees who are missing some of their digits [242]. The authors measured the movements of the remaining healthy digits by means of RFSs mounted onto a glove.…”